In computed tomography (CT), images that reveal the internal structure of an object under study can be obtained by means of penetrating ionizing radiation. Such image, e.g. three-dimensional volumetric image data, can be obtained by applying reconstruction techniques, as known in the art, to projection images obtained by a detector and corresponding to different orientations of a source of penetrating ionizing radiation and the detector with respect to the object, e.g. corresponding to different directions of projecting radiation through the object. The reconstructed image data may be organized in voxels, representative of different positions in the object with respect to a three-dimensional coordinate system, and each voxel may have a value associated therewith, e.g. a greyscale value such as a value expressed in Hounsfield units, that is indicative of attenuation characteristics of the scanned object at the position corresponding to the voxel, e.g. indicative of a radio density, e.g. of a relative radio density.
Spectral CT is an imaging modality that extends the capabilities of conventional CT. In spectral CT, each voxel value may be determined for at least two different qualities of penetrating ionizing radiation. Thus, at least two different attenuation characteristics, e.g. greyscale values, can be assigned to each pixel concurrently. The different qualities of penetrating ionizing radiation may for example differ sufficiently in mean and/or peak photon energy such that the different attenuation characteristics may be subject to discernibly different photoelectric effect and Compton effect contributions, enabling a good differentiation of different materials within the object.
Spectral CT is for example used in medical applications for non-invasively inspecting the internal structure of the body of a subject. Furthermore, spectral CT may be particularly suitable for quantitative imaging applications, since the additional spectral information improves the quantitative information that can be measured about the scanned object and its material composition.
Medical applications of spectral CT, for example vessel analysis or trauma applications, may commonly require a segmentation of bone and/or hard plaque structures in the image data. For example, a segmentation of bone and/or hard plaque structures may enable a selective removal of such structures from the image to enable a better view, or a further automated analysis, of the other structures registered in the image data, such as organs, vessels or other soft tissues of interest.
Segmentation approaches, as known in the art, may be based on time consuming and tedious semi-automatic methods, which may rely of topological background information, such as atlas based methods. Furthermore, interactive editing tools may be used to correct and/or adjust the results of such semi-automatic procedure. However, methods for segmenting bone and/or hard plaque structures known in the art may have the disadvantage of only providing a limited level of precision.
For example, methods known in the art may predominantly rely on simple density or gradient operators. Such methods may provide limited precision and reliability in bone and/or hard plaque segmentation due to different materials having a similar density range while also being near each other. For example bones and vessels may lie in close spatial proximity and may present similar greyscale values in the acquired CT images, e.g. when an intravenously injected contrast agent is used.
For example, accurately and reliably segmenting a bone structure in CT image data, even when using the spectral information gain provided by spectral CT images, may be particularly challenging, since bone structures may have a complex structure and a heterogeneous material composition. For example, some bone and plaque structures, e.g. the trabecular bone, may be complex structures that not only comprise calcium, but also, to a substantial degree, other materials, e.g. soft tissue and adipose tissue. Therefore, the attenuation and spectral characteristics of such bone or plaque structures may be very similar to other structures in the body, e.g. to contrast enhanced organs.
Furthermore, while allowing more information with respect to the constituent materials of the scanned object to be gained as compared to conventional CT, and thus, in principle, enabling a good differentiation of the materials, spectral CT image acquisition may have inherent image quality issues, such as enhanced noise. Thus, bone segmentation algorithms that are based mostly on spectral information may deliver suboptimal results.
For example, WO 2015/083065 discloses a prior art method for segmenting bone in spectral image data. The spectral image data includes at least a first set of image data corresponding to a first energy and second set of image data corresponding to a second different energy. The method includes obtaining the spectral image data. The method further includes extracting a set of features for each voxel in spectral image data. The method further includes determining, for each voxel, a probability that each voxel represents bone structure based on the set of features. The method further includes extracting bone structure from the spectral image data based on the probabilities.